Forecasting & Optimisation Agent

Optimise Your Revenue, Scheduling, or Inventory.

With the S-PRO Forecasting & Optimisation Agent.

A plug-and-play agent that works with your messy data and the external signals you're missing. So your weekly decisions stop costing you.

Dashboard Mar 26 – Apr 31
26,134
Actual Entries
23,314
Baseline
+5.2%
Optimisation
Sales Dynamics — actual vs forecasted vs control

Works With What You Have

Your Data, Plus the Data You're Missing.

Your data is messy. That's expected. We normalise it, enrich it with the external signals that actually drive your demand, use our models to fill in the gaps, and train on your real history.

Bring what you have. We handle the rest.

Weather & market signals — external factors that actually move your demand, woven into every forecast.
Prices & search trends — competitive context and consumer intent captured at signal level.
Model-filled gaps — ML intelligently fills missing history so nothing goes to waste.
Custom-trained on your business — models built on your actual data, not generic priors.
External Signal Enrichment
Weather Prices Market Activity Search Trends
Your Data Sources
Internal Systems Excel / CSV APIs Databases
Optimised Output
Custom ML model trained on your real history — ready to drive your next weekly decision.

How It Works

Three Steps to a Better Forecast.

Step 01

Plug It In

Internal systems, Excel, or both. We connect it. No perfect data required. Whatever format your data lives in, we meet you there.

Step 02

Enrich and Train

External signals and ML models, custom-trained on your business and your history. Your forecast reflects reality, not assumptions.

+5.2%
Optimisation
10/10
Days tested

Step 03

Compare. Then Trust.

Backtested against your current methods on your own data. Proof before you act. You see exactly how the agent performs versus what you do today.

Where It Works

Any Weekly Decision Worth Getting Right.

If you plan, schedule, or allocate on a recurring cadence, the agent fits.

Scheduling
Shifts, slots, and resources aligned to real demand — not guesswork.
Inventory Management
Stock levels and reorders tuned to forecasted demand. Stop over-ordering. Stop running out.
Production Planning
Batches and sequences matched to what sells. Less waste, better flow.
Capacity Planning
Long-term load and expansion decisions grounded in real forecasted demand.
Dynamic Pricing
Prices that move with demand, inventory, and competition in real time.
Budgeting
Revenue and cost scenarios with optimised allocation — built on numbers that matter.

Proof

Already Running at Scale.

Two live deployments. Billions in assets valued. Millions of tickets scheduled. The same agent, applied to very different problems.

Case Study
Multiplex
Scheduling more than 20 million cinema tickets a year for Ukraine's largest cinema chain.
20M+ cinema tickets scheduled per year

Inside the Product

See It Running.

Examples from a cinema deployment (Multiplex). The same workflow applies to any business with recurring planning decisions. The fields change. The pattern does not.

4
Review & Submit
Review your schedule configuration before submitting
Date Range
Start Date
Fri, May 1, 2026
End Date
Fri, May 1, 2026
Selected Films
9 film(s) selected
Inception The Dark Knight Interstellar Oppenheimer Dune: Part Two Mission: Impossible
Constraints & Parameters

Feature 01

Adjustable for Your Business Model and Constraints.

A stepped setup tunes every run to your specific rules before the model generates the optimised output. Pick the time window, the items to include, the constraints. Click once — the agent builds your plan.

Cinemadates, films, constraints, review
WarehouseSKUs, locations, limits
Factorylines, shifts, batch rules
Dashboard — Mar 26 – Apr 31
26,134
Actual Entries
23,314
Baseline
+5.2%
Optimisation
10/10
Experiment Days
Sales Dynamics — actual vs forecasted vs control corridor

Feature 02

Comparison to Control Data Sets.

Every forecast is benchmarked against control data so you see the real effect — not just a number. Peer assets, prior periods, or a held-out A/B group.

+5.2% optimisation effect over 10 days, measured against a 95% corridor built from twin venues
Experimental Days vs Average
Su Mo Tu We Th Fr Sa
Avg. Occupancy by Hour
peak 10 14 18 21

Feature 03

Optimisation After 5 Iterations.

Read-outs show the agent improving against your own baseline over time. Whatever metric matters to your business, tracked by day, hour, or segment.

Day-of-week lift versus the pre-test average
Average tickets per session by hour
Any KPI your business cares about, tracked continuously

Get Started

Ready?

Let's make forecasting the easy part.

  • Works with your existing data — no perfect setup required
  • Backtested against your current methods before you commit
  • Fits any recurring planning decision — scheduling, inventory, pricing
  • Plug-and-play deployment, custom-trained on your history
Start Your Forecast →

Get in touch directly

hi@s-pro.io s-pro.io

How We Engage

From first call to live forecast in weeks — not months.

  • Initial data audit & signal mapping
  • Model training on your real history
  • Backtest comparison to your current approach
  • Live deployment with your team
  • Ongoing iteration & improvement
Book an Intro Call